The rise of artificial intelligence is reshaping almost every part of the business world.
But one area where it’s making things much more complicated — and much more important — is in tech transactions.
Buying or investing in a tech company used to be about reviewing code, patents, contracts, and a handful of trademarks.
Today, if that company builds or uses AI, due diligence has to go much deeper. It’s no longer just about what they own — it’s about how their AI was created, trained, and protected.
Mistakes during diligence can mean buying into hidden risks that could explode later: copyright claims, regulatory problems, or loss of key value if AI rights are not properly secured.
In this article, we’ll break down how AI is changing the way due diligence must be done — and how smart buyers and investors can protect themselves while unlocking the real value of AI assets.
Understanding the New IP Landscape with AI
AI Is Not Just Software — It’s a New Kind of Asset
In traditional tech deals, software due diligence was about lines of code, license agreements, and copyright registrations.
AI has changed that completely.
When a company uses AI, it often owns more than code. It owns models, training datasets, algorithms, and machine-generated outputs.
These are not simple assets to evaluate. Some may be protected by copyright. Others might rely on trade secrets. Still others may have unclear or emerging legal protections, depending on how the AI was built.
If a buyer focuses only on the code and ignores the AI-specific assets, they risk missing huge pieces of the business’s real value — or stepping into hidden risks.
Understanding that AI is a different type of intellectual property is the first step toward smarter due diligence.
Training Data Creates New Ownership Challenges
Every AI model depends on training data.
That data teaches the model how to recognize patterns, make predictions, or generate outputs. But in many companies, the legal rights to that data are unclear.
Some datasets come from public sources. Others are bought under licenses that limit their use. Some are scraped from the internet without clear permission. Still others are built in-house but mixed with third-party information.
If the company being acquired cannot prove it owns or has full rights to the training data, the buyer could inherit major problems.
There may be copyright infringement risks. There may be breach of contract claims from data providers. Worse, regulators in some jurisdictions are starting to scrutinize how AI training data is sourced, creating potential fines and public relations disasters.
Buyers now must dig deep into how every AI model was trained, where the data came from, and what rights attach to it.
Surface-level reviews are no longer enough.
AI Models May Have Unclear Ownership Themselves
It might seem obvious that if a company builds an AI model, it owns that model.
But the reality is messier.
If the model was built using open-source code, commercial tools, or external data under restrictive licenses, ownership could be split or even impaired.
For example, some open-source licenses require that modified versions of models must be shared publicly. Some cloud-based AI training tools impose restrictions on commercial reuse.
In other cases, key parts of the model may have been developed by contractors or freelancers without proper assignment agreements.
If the chain of title over the AI model is broken, the buyer may not have the clear rights they need to use, sell, or improve the model after closing.
This issue can destroy value overnight.
Good due diligence now must treat AI models like a distinct class of IP — one that requires full legal mapping and proof of unbroken ownership before the deal can proceed safely.
How AI-Specific Risks Complicate Tech Transactions
AI Outputs May Not Be Fully Protected
One of the strangest features of AI today is that the outputs it generates — whether text, images, software code, or other content — may not be protectable under traditional copyright law.
In many jurisdictions, copyright requires a human author.
When an AI model creates something autonomously, it is unclear whether that output enjoys legal protection at all.
This matters enormously in a tech transaction.
If a company’s value depends heavily on AI-generated content — for example, AI-written articles, AI-designed graphics, or AI-developed software — the buyer needs to assess whether that content can be protected against copying by competitors.
If it cannot, the business’s competitive advantage may be far weaker than it appears on paper.
Diligence must explore what types of outputs the company’s AI systems produce, how those outputs are used, and what legal protections, if any, exist around them.
This is a new kind of risk — and one that traditional IP diligence processes often overlook.
Regulatory Scrutiny Around AI Is Growing
Governments around the world are racing to regulate AI.
Some proposals focus on transparency — requiring companies to disclose how their AI models are trained and how they make decisions.
Others focus on safety, bias, or accountability for harm caused by AI outputs.
Still others aim to control cross-border data flows and national security risks tied to AI innovation.
In a tech transaction involving AI assets, regulatory risks are no longer hypothetical.
Buyers must assess whether the target’s AI systems comply with emerging AI regulations — and whether they will be adaptable to future rules.
Non-compliance could lead to fines, operational restrictions, or forced disclosures that weaken the value of proprietary models and data.
Smart diligence looks not just at today’s rules, but at the direction regulation is moving — and how resilient the target company’s AI strategy really is.
Ethical Risks Can Create Business Problems Even Without Legal Violations
Even if a company’s AI systems are legal, they may still create business risks.
AI systems that produce biased, discriminatory, or offensive results can generate public backlash.
AI tools that misuse sensitive data — even unintentionally — can destroy customer trust.
And companies that are seen as secretive or careless about their AI use can face reputational damage, partner hesitations, and customer churn.
In today’s environment, ethical AI practices are becoming a real factor in business valuation.
Buyers must ask not only whether the target’s AI systems are compliant, but also whether they are responsible, explainable, and defensible under public scrutiny.
In high-stakes tech M&A deals, reputation risks can have just as much financial impact as legal ones — sometimes more.
How Due Diligence Must Adapt to AI-Driven Assets
Traditional IP Checklists Are No Longer Enough
In tech transactions before AI became dominant, diligence often followed a set pattern.
Review code repositories. Confirm patents and copyrights. Check licensing agreements. Verify open-source compliance.
That world has changed.
Now, buyers must expand their diligence lens to cover areas that old checklists simply miss.
They must look at how AI models were trained, whether data rights are secured, whether outputs are protectable, and whether models carry embedded obligations from open-source or third-party tools.
Without this broader scope, critical issues can be missed — and what looks like a clean acquisition can quickly turn messy after closing.
Diligence teams must be retrained to understand AI-specific IP risks, and the questions they ask must evolve.
Treating AI as just “more software” is a serious mistake.
Technical Audits of AI Systems Are Essential
Because AI assets are complex and multi-layered, pure legal review is not enough.
Technical audits must now be part of the due diligence process.
These audits explore what data was used for training, how models were developed, what external libraries or APIs were incorporated, and how outputs are produced.
They also examine the explainability, reproducibility, and auditability of AI decision-making — factors that are becoming critical in many regulated industries.
Without technical validation, legal assurances about ownership and rights are much harder to verify.
A company may claim to own its AI model, but if the model is built on improperly licensed datasets or heavily modified open-source tools, legal ownership may not survive scrutiny.
Deep technical diligence builds a real foundation for confident acquisition decisions.
It uncovers hidden dependencies, compliance risks, and future costs before they become buyer problems.
Contract Reviews Must Focus on AI-Specific Clauses
In traditional tech diligence, contract review focused on software licenses, IP assignment clauses, and restrictions on sublicensing or transfer.
Now, with AI at the center, contract review must go deeper.
Buyers must examine every agreement that touches data acquisition, data usage, AI tool licensing, development collaborations, cloud service usage, and API integrations.
They must look for clauses that limit how models can be used, commercialized, or modified.
They must confirm that all contributors — employees, contractors, vendors — have assigned their rights properly.
And they must watch for hidden obligations, such as requirements to disclose model architecture, share training improvements, or adhere to open data standards.
AI contracts are often newer, more experimental, and less standardized than traditional software contracts.
That makes careful review even more important — because vague or overly restrictive terms can cripple a buyer’s ability to use or monetize acquired AI assets fully.
Governance Reviews Are Becoming Critical
In AI-driven companies, governance is no longer a luxury.
It is a key part of value protection.
Governance includes internal policies about how data is collected, how AI models are trained, how bias is monitored, how third-party tools are integrated, and how risk is reported and managed.
Buyers must assess whether the target company has real, working governance — not just written policies that nobody follows.
A company with no AI governance may face problems scaling its systems, entering new markets, or complying with future regulations.
Even worse, it may face reputation damage if a hidden issue in its AI operations explodes after acquisition.
Strong governance systems show that a company understands the complexity of AI risks — and that it is prepared to manage them as AI laws, standards, and public expectations evolve.
Good governance does not guarantee safety, but it provides resilience.
And in today’s market, resilience is a valuable asset.
Special Risks in Cross-Border AI Transactions
Many AI deals today involve cross-border transactions.
A company may be headquartered in one country, operate AI systems developed in another, and deploy products globally.
Different countries have different rules about data usage, AI training, consumer transparency, bias mitigation, and national security controls.
A model trained legally in one country might be subject to bans or extra disclosures in another.
Certain types of AI, especially those used in healthcare, finance, or critical infrastructure, are already facing intense regulation in key jurisdictions like the European Union, China, and the United States.
Buyers must analyze not only whether the target’s AI practices are legal today, but whether they are adaptable to the evolving rules across all the markets where they operate — or plan to operate.
Ignoring cross-border AI compliance can turn a seemingly global business into a patchwork of risks that bleed value quickly.
Structuring AI-Specific Protections in Transaction Documents
Why Traditional IP Reps and Warranties Are Not Enough
In most tech transactions, the seller makes promises — called representations and warranties — about the IP being sold.
They promise they own it. They promise it does not infringe others’ rights. They promise no disputes exist.
When AI assets are involved, these traditional promises are necessary but not sufficient.
Buyers now must insist on AI-specific reps and warranties that address training data rights, model development practices, licensing restrictions, and regulatory compliance.
Without AI-specific clauses, many of the biggest risks will fall outside the protections that the agreement offers.
Sellers must be asked directly whether they have clear rights to all training data, whether their AI models contain open-source or third-party content, and whether they are compliant with applicable AI regulations.
Vague promises about “ownership of IP” will not capture the complexity AI brings to the table.
Precise language is needed to match the precise risks.
Allocating Risk Through Indemnities
Even with strong diligence and tight contract language, not all AI risks can be fully discovered before closing.
Some risks are invisible until later — when a regulator tightens AI disclosure rules, when a third-party sues over dataset misuse, or when a hidden flaw in a model creates liability.
That is why indemnities — promises to compensate for certain problems after closing — are even more important when AI is involved.
Buyers should negotiate indemnities that specifically cover breaches of AI-related reps and warranties, infringement related to training data, and failures to comply with AI-specific regulatory requirements.
These indemnities should be crafted carefully, with clear survival periods and, where needed, special escrow or holdback provisions tied to critical risks.
Managing AI risk is not about eliminating it completely. It is about identifying it, pricing it, and structuring real remedies if it materializes later.
Smart dealmakers understand that uncertainty around AI is part of the new landscape — and they prepare accordingly.
Handling Open-Source and Third-Party Components
One of the most persistent risks in AI transactions comes from open-source components and external tools.
Many AI models are built using open-source machine learning libraries, public datasets, or third-party APIs.
Each of these components can carry usage restrictions, disclosure obligations, or licensing terms that affect what the buyer can do with the acquired assets.
In transaction documents, buyers must secure strong disclosures about any open-source code, data, or external tools embedded in AI systems.
They must ensure that the seller has complied with all license requirements — such as attribution, sharing modifications, or restrictions on commercial use.
If material open-source issues exist, buyers should consider remediation plans before closing.
This may involve isolating certain components, replacing libraries, or negotiating special carve-outs to preserve freedom to operate.
Failing to manage these risks properly can create operational headaches and legal liabilities after the deal is complete.
Addressing AI Ethics and Bias in Representations
A newer — but increasingly important — area of concern is ethical use of AI.
Public scrutiny around algorithmic bias, unfair outcomes, and lack of transparency is growing fast.
Some buyers now include representations and warranties in transaction documents that address how AI systems were developed and tested for fairness, accuracy, and explainability.
They ask sellers to affirm that they have policies and processes for mitigating bias, handling complaints, and retraining models where needed.
They also ask about compliance with emerging standards around explainability, such as upcoming European AI regulations.
Including ethics-related reps is not just about public relations. It is about protecting future value.
If a buyer inherits AI systems that produce biased or harmful results, the costs can include regulatory fines, damaged customer trust, and even shareholder lawsuits.
Proactive protections in the deal documents show that the buyer is thinking ahead — and that risk management does not stop at the legal line.
Positioning for Success After the Deal Closes
Integration Planning Must Start During Diligence
In AI-driven transactions, post-closing integration must be part of the plan from day one.
It is not enough to secure rights and move on.
The buyer must build systems to continue managing training data properly, updating models responsibly, monitoring regulatory compliance, and scaling AI systems without breaking legal or ethical rules.
Waiting until after closing to start this planning creates major risks.
Key employees might leave, taking undocumented knowledge with them.
Data might be mishandled, triggering breaches or fines.
Integration teams might misunderstand model dependencies, causing systems to break under new scaling conditions.
The best buyers treat AI integration as a core part of acquisition strategy — not an afterthought.
They align technical, legal, operational, and ethical priorities early, ensuring a smooth transition that protects and grows the value of AI assets over time.
Building a Governance Framework Immediately
Post-closing, AI governance must shift from the seller’s responsibility to the buyer’s hands.
This means setting up internal controls for:
- How new training data is sourced and documented
- How models are modified, tested, and redeployed
- How outputs are monitored for bias, errors, and misuse
- How compliance with evolving AI regulations is tracked and enforced
A good governance framework is not just about avoiding problems.
It enables faster scaling, better innovation, and stronger defensibility of the AI systems that now drive the business.
Buyers who invest early in governance are better positioned to survive legal shifts, public scrutiny, and market competition.
In the AI era, good governance is good business strategy.
Future-Proofing AI Due Diligence for Tomorrow’s Deals
AI Law and Standards Are Moving Fast
Today’s legal framework for AI is just the beginning.
Governments are rapidly drafting new regulations covering transparency, fairness, accountability, and security of AI systems.
Standards for documenting training data, certifying model performance, and disclosing algorithmic decisions are already being tested.
Tomorrow’s AI due diligence will not just ask what rights the company owns. It will also ask whether its AI practices are sustainable under these new rules.
Buyers must start building future-proof diligence frameworks now.
They need to assess how flexible a target’s AI operations are, how easy it will be to update models to meet new compliance demands, and whether ethical risk management is embedded in the business culture.
In AI, being legally clean today is not enough. Survival depends on adaptability tomorrow.
Diligence must evolve to measure not just assets, but readiness.
Scalability Depends on Early Compliance
Companies that handle AI compliance properly from the beginning can scale faster and more safely.
Those that cut corners during early AI development often hit walls later — walls that block expansion into regulated markets, partnerships with major brands, or IPO ambitions.
Buyers should use diligence to test scalability, not just legality.
Are AI datasets fully documented and licensable globally?
Are model training processes auditable?
Are bias monitoring systems in place?
Is privacy embedded into AI systems, not bolted on afterward?
Scalability and compliance now move hand in hand.
Diligence that spots early weaknesses allows buyers to fix issues before they become barriers to growth.
It turns risk into opportunity, and lets acquired companies build stronger, faster.
Reputation Is a Hidden Asset in AI Deals
In AI-heavy tech transactions, reputation often carries as much weight as patents or data rights.
Public trust in AI companies can drive customer adoption, investor support, regulatory goodwill, and partner relationships.
Conversely, AI companies caught mishandling data, creating biased systems, or hiding risks can suffer catastrophic loss of trust — even if they technically comply with the law.
Buyers must assess not just technical and legal compliance, but the target’s standing in the market.
Are they seen as a responsible AI developer?
Do they have thought leadership credibility with regulators and industry bodies?
Have they handled past incidents transparently?
Buying a respected AI brand brings a head start in market access and resilience.
Buying a troubled one can saddle the buyer with endless cleanup and reputational repair.
In a world where AI ethics make headlines, reputation due diligence is not optional.
It is a critical success factor.
Final Takeaways for Winning in AI-Driven Tech Transactions
Rethink Every Assumption About IP
AI has changed the rules of tech IP.
Training data may not be fully owned. Outputs may not be protected. Models may embed open-source obligations or licensing traps.
Buyers must rethink what ownership really means in the AI context.
They must expand diligence beyond patents and copyrights into datasets, model architectures, algorithmic decisions, and governance systems.
Assuming that AI assets behave like traditional software is a mistake.
Winning buyers understand the new rules — and adjust their strategies accordingly.
Build Diligence Teams That Cross Legal, Technical, and Operational Boundaries
Successful AI due diligence requires more than IP lawyers.
It needs technical experts who understand how AI systems are built.
It needs compliance specialists who track evolving AI laws.
It needs data scientists who can assess the quality and ownership of training datasets.
It needs ethicists who understand bias, transparency, and human rights risks.
Building cross-functional diligence teams is no longer a nice-to-have in AI deals.
It is essential to seeing the full picture.
It protects buyers from tunnel vision — and helps surface opportunities others miss.
Think Beyond Closing: Plan for the Whole Lifecycle
Acquiring an AI-driven business is not the end of risk management.
It is the beginning.
Buyers must plan from day one for post-closing integration, compliance monitoring, governance scaling, and reputational defense.
They must assume that AI regulations will tighten.
They must prepare for public scrutiny to rise.
They must embed flexibility, transparency, and accountability into how AI assets are grown after acquisition.
Deals that focus only on closing day leave companies vulnerable.
Deals that build for the long term create enduring competitive advantages.
In the AI era, foresight is more valuable than speed.
Conclusion: Smarter Diligence Is the New Edge in Tech M&A
Artificial intelligence is transforming the landscape of tech transactions.
It is creating new types of IP, new types of risks, and new types of opportunities.
Buyers who cling to old methods will miss critical issues — and lose value fast.
But buyers who evolve, who rethink diligence, who respect the complexity of AI, will thrive.
They will make smarter acquisitions.
They will protect their investments against legal, technical, and ethical risks.
And they will build companies that can lead — not just survive — in the future of AI-driven business.
The game has changed.
The smart players are already adapting.
